Vp facenet

src/align/align_dataset_mtcnn.py

@@ -31,8 +31,8 @@
 import argparse
 import tensorflow as tf
 import numpy as np
-import facenet
-import align.detect_face
+import facenet.src.facenet as facenet
+import detect_face
 import random
 from time import sleep
 
@@ -45,23 +45,23 @@ def main(args):
     src_path,_ = os.path.split(os.path.realpath(__file__))
     facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
     dataset = facenet.get_dataset(args.input_dir)
-    
+
     print('Creating networks and loading parameters')
-    
+
     with tf.Graph().as_default():
         gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
         sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
         with sess.as_default():
-            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
-    
+            pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
+
     minsize = 20 # minimum size of face
     threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
     factor = 0.709 # scale factor
 
     # Add a random key to the filename to allow alignment using multiple processes
     random_key = np.random.randint(0, high=99999)
     bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
-    
+
     with open(bounding_boxes_filename, "w") as text_file:
         nrof_images_total = 0
         nrof_successfully_aligned = 0
@@ -92,8 +92,8 @@ def main(args):
                         if img.ndim == 2:
                             img = facenet.to_rgb(img)
                         img = img[:,:,0:3]
-    
-                        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
+
+                        bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
                         nrof_faces = bounding_boxes.shape[0]
                         if nrof_faces>0:
                             det = bounding_boxes[:,0:4]
@@ -157,3 +157,11 @@ def parse_arguments(argv):
 
 if __name__ == '__main__':
     main(parse_arguments(sys.argv[1:]))
+
+# python align_dataset_mtcnn.py
+# D:\BDBA\SEMESTER_2\Case_Study_1\Datasets\lfw\raw
+# D:\BDBA\SEMESTER_2\Case_Study_1\Datasets\lfw\lfw_mtcnnpy_160
+# --image_size 160
+# --margin 32
+# --random_order
+# --gpu_memory_fraction 0.25

src/create_face_embeddings.py

@@ -0,0 +1,105 @@
+# MIT License
+# 
+# Copyright (c) 2016 David Sandberg
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+# 
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# 
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+import numpy as np
+import argparse
+import facenet
+import lfw
+import os
+import sys
+import math
+import tqdm
+from sklearn import metrics
+from scipy.optimize import brentq
+from scipy import interpolate
+from imutils import paths
+import pickle
+def main(args):
+
+    with tf.Graph().as_default():
+
+        with tf.Session() as sess:
+
+            # Get the paths for the corresponding images
+
+            # vinayak =  ['datasets/kar_Vin_aligned/vinayak/' + f for f in os.listdir('datasets/kar_Vin_aligned/vinayak')]
+            # karthik =  ['datasets/kar_Vin_aligned/karthik/' + f for f in os.listdir('datasets/kar_Vin_aligned/karthik')]
+            # ashish = ['datasets/kar_Vin_aligned/Ashish/' + f for f in os.listdir('datasets/kar_Vin_aligned/Ashish')]
+            # saurabh = ['datasets/kar_Vin_aligned/Saurabh/' + f for f in os.listdir('datasets/kar_Vin_aligned/Saurabh')]
+            # hari = ['datasets/kar_Vin_aligned/Hari/' + f for f in os.listdir('datasets/kar_Vin_aligned/Hari')]
+            print("loading the dataset")
+            # imagePaths = list(paths.list_images(args.dataset_dir))
+            imagepaths = list(paths.list_files(args.dataset_dir))
+            # paths = vinayak+karthik+ashish+saurabh+hari
+            #np.save("images.npy",paths)
+            # Load the model
+            facenet.load_model(args.model)
+
+            # Get input and output tensors
+            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
+            images_placeholder = tf.image.resize_images(images_placeholder,(160,160))
+            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+
+            image_size = args.image_size
+            embedding_size = embeddings.get_shape()[1]
+            extracted_dict = {}
+
+            # Run forward pass to calculate embeddings
+            for i, filename in enumerate(imagepaths):
+
+                images = facenet.load_image(filename, False, False, image_size)
+                feed_dict = { images_placeholder:images, phase_train_placeholder:False }
+                feature_vector = sess.run(embeddings, feed_dict=feed_dict)
+                extracted_dict[filename] =  feature_vector
+                if(i%100 == 0):
+                    print("completed",i," images")
+
+
+            with open('C:/Users/Dell/PycharmProjects/facenet/src/Create_face_embeddings/extracted_dict.pickle','wb') as f:
+                pickle.dump(extracted_dict,f)
+
+
+
+def parse_arguments(argv):  
+    parser = argparse.ArgumentParser()
+
+    #adding a new argument to provide the dataset path
+    parser.add_argument('--dataset_dir', type=str ,
+        help='Path to the dataset containing the images')
+    parser.add_argument('--lfw_batch_size', type=int,
+        help='Number of images to process in a batch in the LFW test set.', default=100)
+    # parser.add_argument('--model', type=str,default='/home/gpuuser/vinayak/models/facenet/src/ckpt/20170512-110547',
+    parser.add_argument('--model', type=str,default='D:/BDBA/SEMESTER_2/Case_Study_1/model/20180402-114759', 
+        help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
+    parser.add_argument('--image_size', type=int,
+        help='Image size (height, width) in pixels.', default=160)
+
+    return parser.parse_args(argv)
+
+if __name__ == '__main__':
+    main(parse_arguments(sys.argv[1:]))

src/facenet.py

@@ -254,6 +254,21 @@ def load_data(image_paths, do_random_crop, do_random_flip, image_size, do_prewhi
         images[i,:,:,:] = img
     return images
 
+def load_image(img, do_random_crop, do_random_flip, image_size, do_prewhiten=True):
+    #nrof_samples = len(image_paths)
+    images = np.zeros((1, image_size, image_size, 3))
+    #for i in range(nrof_samples):
+    img = misc.imread(img)
+    #img = misc.imresize(img,(160,160,3))
+    if img.ndim == 2:
+            img = to_rgb(img)
+    if do_prewhiten:
+            img = prewhiten(img)
+    img = crop(img, do_random_crop, image_size)
+    img = flip(img, do_random_flip)
+    images[:,:,:,:] = img
+    return images
+
 def get_label_batch(label_data, batch_size, batch_index):
     nrof_examples = np.size(label_data, 0)
     j = batch_index*batch_size % nrof_examples

src/generate_pairs.py

@@ -0,0 +1,80 @@
+#! encoding: utf-8
+
+import os
+import random
+
+class GeneratePairs:
+    """
+    Generate the pairs.txt file that is used for training face classifier when calling python `src/train_softmax.py`.
+    Or others' python scripts that needs the file of pairs.txt.
+
+    Doc Reference: http://vis-www.cs.umass.edu/lfw/README.txt
+    """
+
+    def __init__(self, data_dir, pairs_filepath, img_ext):
+        """
+        Parameter data_dir, is your data directory.
+        Parameter pairs_filepath, where is the pairs.txt that belongs to.
+        Parameter img_ext, is the image data extension for all of your image data.
+        """
+        self.data_dir = data_dir
+        self.pairs_filepath = pairs_filepath
+        self.img_ext = img_ext
+
+
+    def generate(self):
+        self._generate_matches_pairs()
+        self._generate_mismatches_pairs()
+
+
+    def _generate_matches_pairs(self):
+        """
+        Generate all matches pairs
+        """
+        for name in os.listdir(self.data_dir):
+            if name == ".DS_Store":
+                continue
+
+            a = []
+            for file in os.listdir(self.data_dir + name):
+                if file == ".DS_Store":
+                    continue
+                a.append(file)
+
+            with open(self.pairs_filepath, "a") as f:
+                for i in range(3):
+                    temp = random.choice(a).split("_") # This line may vary depending on how your images are named.
+                    w = temp[0] + "_" + temp[1]
+                    l = random.choice(a).split("_")[2].lstrip("0").rstrip(self.img_ext)
+                    r = random.choice(a).split("_")[2].lstrip("0").rstrip(self.img_ext)
+                    f.write(w + "\t" + l + "\t" + r + "\n")
+
+
+    def _generate_mismatches_pairs(self):
+        """
+        Generate all mismatches pairs
+        """
+        for i, name in enumerate(os.listdir(self.data_dir)):
+            if name == ".DS_Store":
+                continue
+
+            remaining = os.listdir(self.data_dir)
+            remaining = [f_n for f_n in remaining if f_n != ".DS_Store"]
+            # del remaining[i] # deletes the file from the list, so that it is not chosen again
+            other_dir = random.choice(remaining)
+            with open(self.pairs_filepath, "a") as f:
+                for i in range(3):
+                    file1 = random.choice(os.listdir(self.data_dir + name))
+                    file2 = random.choice(os.listdir(self.data_dir + other_dir))
+                    f.write(name + "\t" + file1.split("_")[2].lstrip("0").rstrip(self.img_ext) + "\t")
+                f.write("\n")
+
+
+if __name__ == '__main__':
+    # data_dir = "D:/BDBA/SEMESTER_2/Case_Study_1/Datasets/my_dataset/lfw_mtcnnpy_train_160/"
+    data_dir = "D:/BDBA/SEMESTER_2/Case_Study_1/Datasets/my_dataset/lfw_mtcnnpy_test_160/"
+    pairs_filepath = "C:/Users/Dell/PycharmProjects/facenet/src/pairs.txt"
+    img_ext = ".jpg"
+    generatePairs = GeneratePairs(data_dir, pairs_filepath, img_ext)
+    generatePairs.generate()
+

src/lfw.py

@@ -62,7 +62,7 @@ def get_paths(lfw_dir, pairs):
         else:
             nrof_skipped_pairs += 1
     if nrof_skipped_pairs>0:
-        print('Skipped %d image pairs' % nrof_skipped_pairs)
+        print('Skipped %d image pairs hahahaha' % nrof_skipped_pairs)
 
     return path_list, issame_list
 

src/rest-server.py

@@ -0,0 +1,76 @@
+#!flask/bin/python
+################################################################################################################################
+#------------------------------------------------------------------------------------------------------------------------------                                                                                                                             
+# This file implements the REST layer. It uses flask micro framework for server implementation. Calls from front end reaches 
+# here as json and being branched out to each projects. Basic level of validation is also being done in this file. #                                                                                                                                  	       
+#-------------------------------------------------------------------------------------------------------------------------------                                                                                                                              
+################################################################################################################################
+from flask import Flask, jsonify, abort, request, make_response, url_for,redirect, render_template
+# from flask.ext.httpauth import HTTPBasicAuth
+from flask_httpauth import HTTPBasicAuth
+from werkzeug.utils import secure_filename
+import os
+import sys
+import random
+from tensorflow.python.platform import gfile
+from six import iteritems
+sys.path.append('..')
+import numpy as np
+import retrieve
+import detect_face
+import tensorflow as tf
+import pickle
+from tensorflow.python.platform import gfile
+app = Flask(__name__, static_url_path = "")
+
+auth = HTTPBasicAuth()
+
+#==============================================================================================================================
+#                                                                                                                              
+#    Loading the stored face embedding vectors for image retrieval                                                                 
+#                                                                          						        
+#                                                                                                                              
+#==============================================================================================================================
+# with open('../lib/src/face_embeddings.pickle','rb') as f:
+# 	feature_array = pickle.load(f) 
+
+with open('C:/Users/Dell/PycharmProjects/facenet/src/Create_face_embeddings/extracted_dict.pickle','rb') as f:
+	feature_array = pickle.load(f) 					    	
+
+# model_exp = '../lib/src/ckpt/20170512-110547'
+# graph_fr = tf.Graph()
+# sess_fr = tf.Session(graph=graph_fr)
+# with graph_fr.as_default():
+# 	saverf = tf.train.import_meta_graph(os.path.join(model_exp, 'model-20170512-110547.meta'))
+# 	saverf.restore(sess_fr, os.path.join(model_exp, 'model-20170512-110547.ckpt-250000'))
+# 	pnet, rnet, onet = detect_face.create_mtcnn(sess_fr, None)
+
+model_exp = 'D:/BDBA/SEMESTER_2/Case_Study_1/model/20180402-114759'
+graph_fr = tf.Graph()
+sess_fr = tf.Session(graph=graph_fr)
+with graph_fr.as_default():
+	saverf = tf.train.import_meta_graph(os.path.join(model_exp, 'model-20180402-114759.meta'))
+	saverf.restore(sess_fr, os.path.join(model_exp, 'model-20180402-114759.ckpt-275.ckpt-275'))
+	pnet, rnet, onet = detect_face.create_mtcnn(sess_fr, None)
+#==============================================================================================================================
+#                                                                                                                              
+#  This function is used to do the face recognition from video camera                                                          
+#                                                                                                 
+#                                                                                                                              
+#==============================================================================================================================
+@app.route('/facerecognitionLive', methods=['GET', 'POST'])
+def face_det():
+
+    retrieve.recognize_face(sess_fr,pnet, rnet, onet,feature_array)
+
+#==============================================================================================================================
+#                                                                                                                              
+#                                           Main function                                                        	            #						     									       
+#  				                                                                                                
+#==============================================================================================================================
+@app.route("/")
+def main():
+
+    return render_template("main.html")   
+if __name__ == '__main__':
+    app.run(debug = True, host= '0.0.0.0')